Overview

Dataset statistics

Number of variables49
Number of observations25286
Missing cells0
Missing cells (%)0.0%
Duplicate rows102
Duplicate rows (%)0.4%
Total size in memory9.4 MiB
Average record size in memory388.0 B

Variable types

Numeric40
Categorical9

Alerts

Z1_CommandAcceleration has constant value "0.0" Constant
Z1_CurrentFeedback has constant value "0.0" Constant
Z1_DCBusVoltage has constant value "0.0" Constant
Z1_OutputCurrent has constant value "0.0" Constant
Z1_OutputVoltage has constant value "0.0" Constant
S1_SystemInertia has constant value "12.0" Constant
Dataset has 102 (0.4%) duplicate rowsDuplicates
X1_ActualPosition is highly correlated with X1_CommandPositionHigh correlation
X1_ActualVelocity is highly correlated with X1_CommandVelocity and 1 other fieldsHigh correlation
X1_CommandPosition is highly correlated with X1_ActualPositionHigh correlation
X1_CommandVelocity is highly correlated with X1_ActualVelocity and 1 other fieldsHigh correlation
X1_CurrentFeedback is highly correlated with X1_ActualVelocity and 1 other fieldsHigh correlation
X1_DCBusVoltage is highly correlated with X1_OutputVoltage and 5 other fieldsHigh correlation
X1_OutputCurrent is highly correlated with Y1_OutputCurrent and 6 other fieldsHigh correlation
X1_OutputVoltage is highly correlated with X1_DCBusVoltage and 4 other fieldsHigh correlation
X1_OutputPower is highly correlated with X1_DCBusVoltage and 1 other fieldsHigh correlation
Y1_ActualPosition is highly correlated with Y1_CommandPositionHigh correlation
Y1_ActualVelocity is highly correlated with Y1_CommandVelocity and 1 other fieldsHigh correlation
Y1_CommandPosition is highly correlated with Y1_ActualPositionHigh correlation
Y1_CommandVelocity is highly correlated with Y1_ActualVelocity and 1 other fieldsHigh correlation
Y1_CurrentFeedback is highly correlated with Y1_ActualVelocity and 1 other fieldsHigh correlation
Y1_DCBusVoltage is highly correlated with Y1_OutputVoltage and 4 other fieldsHigh correlation
Y1_OutputCurrent is highly correlated with X1_OutputCurrent and 8 other fieldsHigh correlation
Y1_OutputVoltage is highly correlated with Y1_DCBusVoltage and 3 other fieldsHigh correlation
Y1_OutputPower is highly correlated with Y1_DCBusVoltage and 1 other fieldsHigh correlation
Z1_ActualPosition is highly correlated with X1_DCBusVoltage and 12 other fieldsHigh correlation
Z1_ActualVelocity is highly correlated with Z1_ActualAccelerationHigh correlation
Z1_ActualAcceleration is highly correlated with Z1_ActualVelocityHigh correlation
Z1_CommandPosition is highly correlated with X1_DCBusVoltage and 12 other fieldsHigh correlation
S1_ActualPosition is highly correlated with S1_CommandPositionHigh correlation
S1_ActualVelocity is highly correlated with Y1_OutputCurrent and 7 other fieldsHigh correlation
S1_CommandPosition is highly correlated with S1_ActualPositionHigh correlation
S1_CommandVelocity is highly correlated with X1_DCBusVoltage and 15 other fieldsHigh correlation
S1_CurrentFeedback is highly correlated with X1_OutputCurrent and 10 other fieldsHigh correlation
S1_DCBusVoltage is highly correlated with X1_OutputCurrent and 10 other fieldsHigh correlation
S1_OutputCurrent is highly correlated with X1_DCBusVoltage and 11 other fieldsHigh correlation
S1_OutputVoltage is highly correlated with X1_OutputCurrent and 11 other fieldsHigh correlation
S1_OutputPower is highly correlated with X1_OutputCurrent and 10 other fieldsHigh correlation
M1_sequence_number is highly correlated with Z1_ActualPosition and 9 other fieldsHigh correlation
M1_CURRENT_FEEDRATE is highly correlated with Z1_ActualPosition and 8 other fieldsHigh correlation
X1_ActualPosition is highly correlated with X1_CommandPosition and 13 other fieldsHigh correlation
X1_ActualVelocity is highly correlated with X1_CommandVelocity and 1 other fieldsHigh correlation
X1_CommandPosition is highly correlated with X1_ActualPosition and 13 other fieldsHigh correlation
X1_CommandVelocity is highly correlated with X1_ActualVelocity and 1 other fieldsHigh correlation
X1_CurrentFeedback is highly correlated with X1_ActualVelocity and 1 other fieldsHigh correlation
X1_DCBusVoltage is highly correlated with X1_ActualPosition and 15 other fieldsHigh correlation
X1_OutputCurrent is highly correlated with Y1_OutputCurrent and 7 other fieldsHigh correlation
X1_OutputVoltage is highly correlated with X1_DCBusVoltage and 1 other fieldsHigh correlation
X1_OutputPower is highly correlated with X1_DCBusVoltage and 1 other fieldsHigh correlation
Y1_ActualPosition is highly correlated with X1_ActualPosition and 13 other fieldsHigh correlation
Y1_ActualVelocity is highly correlated with Y1_CommandVelocity and 5 other fieldsHigh correlation
Y1_CommandPosition is highly correlated with X1_ActualPosition and 13 other fieldsHigh correlation
Y1_CommandVelocity is highly correlated with Y1_ActualVelocity and 5 other fieldsHigh correlation
Y1_CurrentFeedback is highly correlated with Y1_ActualVelocity and 1 other fieldsHigh correlation
Y1_DCBusVoltage is highly correlated with Y1_OutputVoltage and 1 other fieldsHigh correlation
Y1_OutputCurrent is highly correlated with X1_ActualPosition and 14 other fieldsHigh correlation
Y1_OutputVoltage is highly correlated with Y1_ActualVelocity and 3 other fieldsHigh correlation
Y1_OutputPower is highly correlated with Y1_ActualVelocity and 5 other fieldsHigh correlation
Z1_ActualPosition is highly correlated with X1_ActualPosition and 15 other fieldsHigh correlation
Z1_ActualVelocity is highly correlated with Y1_ActualVelocity and 3 other fieldsHigh correlation
Z1_CommandPosition is highly correlated with X1_ActualPosition and 15 other fieldsHigh correlation
Z1_CommandVelocity is highly correlated with Y1_ActualVelocity and 3 other fieldsHigh correlation
S1_ActualPosition is highly correlated with S1_CommandPositionHigh correlation
S1_ActualVelocity is highly correlated with X1_ActualPosition and 16 other fieldsHigh correlation
S1_CommandPosition is highly correlated with S1_ActualPositionHigh correlation
S1_CommandVelocity is highly correlated with X1_ActualPosition and 16 other fieldsHigh correlation
S1_CurrentFeedback is highly correlated with X1_ActualPosition and 16 other fieldsHigh correlation
S1_DCBusVoltage is highly correlated with X1_ActualPosition and 16 other fieldsHigh correlation
S1_OutputCurrent is highly correlated with X1_ActualPosition and 16 other fieldsHigh correlation
S1_OutputVoltage is highly correlated with X1_ActualPosition and 16 other fieldsHigh correlation
S1_OutputPower is highly correlated with X1_ActualPosition and 16 other fieldsHigh correlation
M1_sequence_number is highly correlated with Z1_ActualPosition and 9 other fieldsHigh correlation
M1_CURRENT_FEEDRATE is highly correlated with Z1_ActualPosition and 9 other fieldsHigh correlation
X1_ActualPosition is highly correlated with X1_CommandPositionHigh correlation
X1_ActualVelocity is highly correlated with X1_CommandVelocity and 1 other fieldsHigh correlation
X1_CommandPosition is highly correlated with X1_ActualPositionHigh correlation
X1_CommandVelocity is highly correlated with X1_ActualVelocity and 1 other fieldsHigh correlation
X1_CurrentFeedback is highly correlated with X1_ActualVelocity and 1 other fieldsHigh correlation
X1_DCBusVoltage is highly correlated with X1_OutputVoltage and 1 other fieldsHigh correlation
X1_OutputCurrent is highly correlated with Y1_OutputCurrent and 2 other fieldsHigh correlation
X1_OutputVoltage is highly correlated with X1_DCBusVoltage and 1 other fieldsHigh correlation
X1_OutputPower is highly correlated with X1_DCBusVoltage and 1 other fieldsHigh correlation
Y1_ActualPosition is highly correlated with Y1_CommandPositionHigh correlation
Y1_ActualVelocity is highly correlated with Y1_CommandVelocity and 1 other fieldsHigh correlation
Y1_CommandPosition is highly correlated with Y1_ActualPositionHigh correlation
Y1_CommandVelocity is highly correlated with Y1_ActualVelocity and 1 other fieldsHigh correlation
Y1_CurrentFeedback is highly correlated with Y1_ActualVelocity and 1 other fieldsHigh correlation
Y1_DCBusVoltage is highly correlated with Y1_OutputVoltage and 1 other fieldsHigh correlation
Y1_OutputCurrent is highly correlated with X1_OutputCurrent and 2 other fieldsHigh correlation
Y1_OutputVoltage is highly correlated with Y1_DCBusVoltage and 1 other fieldsHigh correlation
Y1_OutputPower is highly correlated with Y1_DCBusVoltage and 1 other fieldsHigh correlation
Z1_ActualPosition is highly correlated with Z1_CommandPosition and 3 other fieldsHigh correlation
Z1_ActualVelocity is highly correlated with Z1_ActualAccelerationHigh correlation
Z1_ActualAcceleration is highly correlated with Z1_ActualVelocityHigh correlation
Z1_CommandPosition is highly correlated with Z1_ActualPosition and 3 other fieldsHigh correlation
S1_ActualPosition is highly correlated with S1_CommandPositionHigh correlation
S1_ActualVelocity is highly correlated with S1_CommandVelocityHigh correlation
S1_CommandPosition is highly correlated with S1_ActualPositionHigh correlation
S1_CommandVelocity is highly correlated with X1_OutputCurrent and 11 other fieldsHigh correlation
S1_CurrentFeedback is highly correlated with S1_CommandVelocity and 2 other fieldsHigh correlation
S1_DCBusVoltage is highly correlated with S1_CommandVelocity and 3 other fieldsHigh correlation
S1_OutputCurrent is highly correlated with X1_OutputCurrent and 3 other fieldsHigh correlation
S1_OutputVoltage is highly correlated with S1_CommandVelocityHigh correlation
S1_OutputPower is highly correlated with S1_CommandVelocity and 2 other fieldsHigh correlation
M1_sequence_number is highly correlated with Z1_ActualPosition and 2 other fieldsHigh correlation
M1_CURRENT_FEEDRATE is highly correlated with Z1_ActualPosition and 2 other fieldsHigh correlation
Z1_CommandAcceleration is highly correlated with Z1_CurrentFeedback and 7 other fieldsHigh correlation
Z1_CurrentFeedback is highly correlated with Z1_CommandAcceleration and 7 other fieldsHigh correlation
Z1_OutputCurrent is highly correlated with Z1_CommandAcceleration and 7 other fieldsHigh correlation
S1_SystemInertia is highly correlated with Z1_CommandAcceleration and 7 other fieldsHigh correlation
Z1_OutputVoltage is highly correlated with Z1_CommandAcceleration and 7 other fieldsHigh correlation
S1_CommandAcceleration is highly correlated with Z1_CommandAcceleration and 5 other fieldsHigh correlation
M1_CURRENT_PROGRAM_NUMBER is highly correlated with Z1_CommandAcceleration and 5 other fieldsHigh correlation
Z1_DCBusVoltage is highly correlated with Z1_CommandAcceleration and 7 other fieldsHigh correlation
target is highly correlated with Z1_CommandAcceleration and 5 other fieldsHigh correlation
X1_ActualPosition is highly correlated with X1_ActualVelocity and 32 other fieldsHigh correlation
X1_ActualVelocity is highly correlated with X1_ActualPosition and 22 other fieldsHigh correlation
X1_ActualAcceleration is highly correlated with X1_ActualPosition and 6 other fieldsHigh correlation
X1_CommandPosition is highly correlated with X1_ActualPosition and 32 other fieldsHigh correlation
X1_CommandVelocity is highly correlated with X1_ActualPosition and 25 other fieldsHigh correlation
X1_CommandAcceleration is highly correlated with X1_ActualPosition and 7 other fieldsHigh correlation
X1_CurrentFeedback is highly correlated with X1_ActualPosition and 24 other fieldsHigh correlation
X1_DCBusVoltage is highly correlated with X1_ActualPosition and 29 other fieldsHigh correlation
X1_OutputCurrent is highly correlated with X1_ActualPosition and 18 other fieldsHigh correlation
X1_OutputVoltage is highly correlated with X1_ActualPosition and 29 other fieldsHigh correlation
X1_OutputPower is highly correlated with X1_ActualPosition and 20 other fieldsHigh correlation
Y1_ActualPosition is highly correlated with X1_ActualPosition and 30 other fieldsHigh correlation
Y1_ActualVelocity is highly correlated with X1_ActualPosition and 21 other fieldsHigh correlation
Y1_ActualAcceleration is highly correlated with Y1_CurrentFeedback and 2 other fieldsHigh correlation
Y1_CommandPosition is highly correlated with X1_ActualPosition and 30 other fieldsHigh correlation
Y1_CommandVelocity is highly correlated with X1_ActualPosition and 21 other fieldsHigh correlation
Y1_CommandAcceleration is highly correlated with X1_ActualVelocity and 5 other fieldsHigh correlation
Y1_CurrentFeedback is highly correlated with X1_ActualPosition and 27 other fieldsHigh correlation
Y1_DCBusVoltage is highly correlated with X1_ActualPosition and 29 other fieldsHigh correlation
Y1_OutputCurrent is highly correlated with X1_ActualPosition and 31 other fieldsHigh correlation
Y1_OutputVoltage is highly correlated with X1_ActualPosition and 31 other fieldsHigh correlation
Y1_OutputPower is highly correlated with X1_ActualPosition and 19 other fieldsHigh correlation
Z1_ActualPosition is highly correlated with X1_ActualPosition and 31 other fieldsHigh correlation
Z1_ActualVelocity is highly correlated with X1_ActualPosition and 17 other fieldsHigh correlation
Z1_ActualAcceleration is highly correlated with Z1_ActualVelocity and 1 other fieldsHigh correlation
Z1_CommandPosition is highly correlated with X1_ActualPosition and 31 other fieldsHigh correlation
Z1_CommandVelocity is highly correlated with X1_ActualPosition and 17 other fieldsHigh correlation
S1_ActualPosition is highly correlated with Y1_OutputCurrent and 2 other fieldsHigh correlation
S1_ActualVelocity is highly correlated with X1_ActualPosition and 22 other fieldsHigh correlation
S1_ActualAcceleration is highly correlated with X1_ActualPosition and 16 other fieldsHigh correlation
S1_CommandPosition is highly correlated with Y1_OutputCurrent and 2 other fieldsHigh correlation
S1_CommandVelocity is highly correlated with X1_ActualPosition and 23 other fieldsHigh correlation
S1_CurrentFeedback is highly correlated with X1_ActualPosition and 22 other fieldsHigh correlation
S1_DCBusVoltage is highly correlated with X1_ActualPosition and 17 other fieldsHigh correlation
S1_OutputCurrent is highly correlated with X1_ActualPosition and 25 other fieldsHigh correlation
S1_OutputVoltage is highly correlated with X1_ActualPosition and 23 other fieldsHigh correlation
S1_OutputPower is highly correlated with X1_ActualPosition and 22 other fieldsHigh correlation
M1_CURRENT_PROGRAM_NUMBER is highly correlated with X1_DCBusVoltage and 2 other fieldsHigh correlation
M1_sequence_number is highly correlated with X1_ActualPosition and 21 other fieldsHigh correlation
M1_CURRENT_FEEDRATE is highly correlated with X1_ActualPosition and 30 other fieldsHigh correlation
X1_ActualVelocity has 3451 (13.6%) zeros Zeros
X1_ActualAcceleration has 3453 (13.7%) zeros Zeros
X1_CommandVelocity has 11413 (45.1%) zeros Zeros
X1_CommandAcceleration has 18490 (73.1%) zeros Zeros
X1_OutputVoltage has 1480 (5.9%) zeros Zeros
X1_OutputPower has 2153 (8.5%) zeros Zeros
Y1_ActualVelocity has 4987 (19.7%) zeros Zeros
Y1_ActualAcceleration has 4571 (18.1%) zeros Zeros
Y1_CommandVelocity has 14647 (57.9%) zeros Zeros
Y1_CommandAcceleration has 18407 (72.8%) zeros Zeros
Y1_OutputVoltage has 1480 (5.9%) zeros Zeros
Y1_OutputPower has 3697 (14.6%) zeros Zeros
Z1_ActualVelocity has 13878 (54.9%) zeros Zeros
Z1_ActualAcceleration has 10935 (43.2%) zeros Zeros
Z1_CommandVelocity has 24031 (95.0%) zeros Zeros
S1_ActualVelocity has 2142 (8.5%) zeros Zeros
S1_ActualAcceleration has 1007 (4.0%) zeros Zeros
S1_CommandVelocity has 6902 (27.3%) zeros Zeros
S1_DCBusVoltage has 770 (3.0%) zeros Zeros
S1_OutputVoltage has 6876 (27.2%) zeros Zeros
S1_OutputPower has 2045 (8.1%) zeros Zeros
M1_sequence_number has 5706 (22.6%) zeros Zeros
Machining_Process has 2585 (10.2%) zeros Zeros

Reproduction

Analysis started2022-08-25 12:56:16.285780
Analysis finished2022-08-25 13:01:47.682725
Duration5 minutes and 31.4 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

X1_ActualPosition
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct58
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.0520446
Minimum141
Maximum198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size197.7 KiB
2022-08-25T18:31:47.776222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum141
5-th percentile141
Q1145
median153
Q3162
95-th percentile198
Maximum198
Range57
Interquartile range (IQR)17

Descriptive statistics

Standard deviation19.33087267
Coefficient of variation (CV)0.1215380332
Kurtosis0.07731529598
Mean159.0520446
Median Absolute Deviation (MAD)9
Skewness1.2197207
Sum4021790
Variance373.6826383
MonotonicityNot monotonic
2022-08-25T18:31:47.895448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1984360
17.2%
1413627
14.3%
1622117
 
8.4%
1591634
 
6.5%
1511492
 
5.9%
1441101
 
4.4%
142950
 
3.8%
145903
 
3.6%
146719
 
2.8%
148692
 
2.7%
Other values (48)7691
30.4%
ValueCountFrequency (%)
1413627
14.3%
142950
 
3.8%
143530
 
2.1%
1441101
 
4.4%
145903
 
3.6%
146719
 
2.8%
147637
 
2.5%
148692
 
2.7%
149666
 
2.6%
150639
 
2.5%
ValueCountFrequency (%)
1984360
17.2%
19712
 
< 0.1%
19611
 
< 0.1%
1957
 
< 0.1%
19412
 
< 0.1%
1938
 
< 0.1%
19215
 
0.1%
1916
 
< 0.1%
19013
 
0.1%
1897
 
< 0.1%

X1_ActualVelocity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct713
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.4906226766
Minimum-18.1
Maximum12.2
Zeros3451
Zeros (%)13.6%
Negative11736
Negative (%)46.4%
Memory size197.7 KiB
2022-08-25T18:31:48.010659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-18.1
5-th percentile-6.13
Q1-2.05
median0
Q30.2
95-th percentile5.93
Maximum12.2
Range30.3
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation4.550623625
Coefficient of variation (CV)-9.275200356
Kurtosis5.662747773
Mean-0.4906226766
Median Absolute Deviation (MAD)1.13
Skewness-1.258817643
Sum-12405.885
Variance20.70817537
MonotonicityNot monotonic
2022-08-25T18:31:48.111118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03451
 
13.6%
0.0251058
 
4.2%
-0.0251013
 
4.0%
-0.05606
 
2.4%
0.05549
 
2.2%
-0.075516
 
2.0%
12.2511
 
2.0%
0.075455
 
1.8%
-0.175426
 
1.7%
0.175387
 
1.5%
Other values (703)16314
64.5%
ValueCountFrequency (%)
-18.1280
1.1%
-18122
0.5%
-17.9130
0.5%
-17.8117
0.5%
-17.725
 
0.1%
-17.612
 
< 0.1%
-17.51
 
< 0.1%
-17.33
 
< 0.1%
-17.26
 
< 0.1%
-17.11
 
< 0.1%
ValueCountFrequency (%)
12.2511
2.0%
12.119
 
0.1%
127
 
< 0.1%
11.921
 
0.1%
11.812
 
< 0.1%
11.714
 
0.1%
11.63
 
< 0.1%
11.52
 
< 0.1%
11.42
 
< 0.1%
11.34
 
< 0.1%

X1_ActualAcceleration
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct41
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.9378885549
Minimum-138
Maximum113
Zeros3453
Zeros (%)13.7%
Negative11005
Negative (%)43.5%
Memory size197.7 KiB
2022-08-25T18:31:48.234660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-138
5-th percentile-87.5
Q1-31.3
median0
Q325
95-th percentile87.5
Maximum113
Range251
Interquartile range (IQR)56.3

Descriptive statistics

Standard deviation49.76068965
Coefficient of variation (CV)-53.05607942
Kurtosis0.1909623189
Mean-0.9378885549
Median Absolute Deviation (MAD)31.3
Skewness-0.09991297317
Sum-23715.45
Variance2476.126235
MonotonicityNot monotonic
2022-08-25T18:31:48.349687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
03453
 
13.7%
6.251545
 
6.1%
-6.251406
 
5.6%
12.51215
 
4.8%
-12.51209
 
4.8%
18.8964
 
3.8%
-18.8952
 
3.8%
-25946
 
3.7%
-37.5874
 
3.5%
25809
 
3.2%
Other values (31)11913
47.1%
ValueCountFrequency (%)
-138283
1.1%
-13140
 
0.2%
-12531
 
0.1%
-11943
 
0.2%
-113104
 
0.4%
-106161
0.6%
-100210
0.8%
-93.7262
1.0%
-87.5324
1.3%
-81.2306
1.2%
ValueCountFrequency (%)
113511
2.0%
106119
 
0.5%
100182
 
0.7%
93.7269
1.1%
87.5289
1.1%
81.2321
1.3%
75358
1.4%
68.7422
1.7%
62.5510
2.0%
56.3528
2.1%

X1_CommandPosition
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct58
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.0507
Minimum141
Maximum198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size197.7 KiB
2022-08-25T18:31:48.483124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum141
5-th percentile141
Q1145
median153
Q3162
95-th percentile198
Maximum198
Range57
Interquartile range (IQR)17

Descriptive statistics

Standard deviation19.33114426
Coefficient of variation (CV)0.1215407682
Kurtosis0.07735687667
Mean159.0507
Median Absolute Deviation (MAD)9
Skewness1.219764791
Sum4021756
Variance373.6931383
MonotonicityNot monotonic
2022-08-25T18:31:48.597455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1984358
17.2%
1413631
14.4%
1622118
 
8.4%
1591633
 
6.5%
1511494
 
5.9%
1441096
 
4.3%
142944
 
3.7%
145912
 
3.6%
146708
 
2.8%
148685
 
2.7%
Other values (48)7707
30.5%
ValueCountFrequency (%)
1413631
14.4%
142944
 
3.7%
143536
 
2.1%
1441096
 
4.3%
145912
 
3.6%
146708
 
2.8%
147644
 
2.5%
148685
 
2.7%
149667
 
2.6%
150640
 
2.5%
ValueCountFrequency (%)
1984358
17.2%
19714
 
0.1%
19610
 
< 0.1%
1959
 
< 0.1%
19411
 
< 0.1%
1938
 
< 0.1%
19214
 
0.1%
1917
 
< 0.1%
19013
 
0.1%
1897
 
< 0.1%

X1_CommandVelocity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1650
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.4891507783
Minimum-17.9
Maximum12
Zeros11413
Zeros (%)45.1%
Negative7647
Negative (%)30.2%
Memory size197.7 KiB
2022-08-25T18:31:48.717429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-17.9
5-th percentile-6
Q1-2.05
median0
Q30
95-th percentile6
Maximum12
Range29.9
Interquartile range (IQR)2.05

Descriptive statistics

Standard deviation4.53629514
Coefficient of variation (CV)-9.273817688
Kurtosis5.610488364
Mean-0.4891507783
Median Absolute Deviation (MAD)1.15
Skewness-1.267098625
Sum-12368.66658
Variance20.57797359
MonotonicityNot monotonic
2022-08-25T18:31:48.829120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011413
45.1%
-32551
 
10.1%
32249
 
8.9%
-6868
 
3.4%
6768
 
3.0%
-17.9679
 
2.7%
12585
 
2.3%
-12105
 
0.4%
-1584
 
0.3%
-2.9962
 
0.2%
Other values (1640)5922
23.4%
ValueCountFrequency (%)
-17.9679
2.7%
-17.81
 
< 0.1%
-17.75
 
< 0.1%
-17.61
 
< 0.1%
-17.55
 
< 0.1%
-17.32
 
< 0.1%
-17.23
 
< 0.1%
-16.92
 
< 0.1%
-16.52
 
< 0.1%
-16.42
 
< 0.1%
ValueCountFrequency (%)
12585
2.3%
11.94
 
< 0.1%
11.83
 
< 0.1%
11.71
 
< 0.1%
11.65
 
< 0.1%
11.51
 
< 0.1%
11.34
 
< 0.1%
11.21
 
< 0.1%
11.12
 
< 0.1%
113
 
< 0.1%

X1_CommandAcceleration
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct1889
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.4176131467
Minimum-53.50675
Maximum13.409
Zeros18490
Zeros (%)73.1%
Negative3051
Negative (%)12.1%
Memory size197.7 KiB
2022-08-25T18:31:48.957239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-53.50675
5-th percentile-2.6875
Q10
median0
Q30
95-th percentile3.26
Maximum13.409
Range66.91575
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.542905094
Coefficient of variation (CV)-15.66738295
Kurtosis47.47198424
Mean-0.4176131467
Median Absolute Deviation (MAD)0
Skewness-6.137413787
Sum-10559.76603
Variance42.80960706
MonotonicityNot monotonic
2022-08-25T18:31:49.086490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
018490
73.1%
13.409506
 
2.0%
-53.50675253
 
1.0%
-9.54 × 10-5132
 
0.5%
9.54 × 10-5114
 
0.5%
0.00019152
 
0.2%
-0.00019140
 
0.2%
-2.2630
 
0.1%
-3.4329
 
0.1%
2.2629
 
0.1%
Other values (1879)5611
 
22.2%
ValueCountFrequency (%)
-53.50675253
1.0%
-53.51
 
< 0.1%
-53.42
 
< 0.1%
-53.21
 
< 0.1%
-53.12
 
< 0.1%
-531
 
< 0.1%
-52.71
 
< 0.1%
-52.51
 
< 0.1%
-52.11
 
< 0.1%
-51.41
 
< 0.1%
ValueCountFrequency (%)
13.409506
2.0%
13.41
 
< 0.1%
13.35
 
< 0.1%
13.24
 
< 0.1%
13.11
 
< 0.1%
134
 
< 0.1%
12.95
 
< 0.1%
12.83
 
< 0.1%
12.74
 
< 0.1%
12.65
 
< 0.1%

X1_CurrentFeedback
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1979
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.499259302
Minimum-8.07
Maximum6.9309
Zeros0
Zeros (%)0.0%
Negative14249
Negative (%)56.4%
Memory size197.7 KiB
2022-08-25T18:31:49.206230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-8.07
5-th percentile-6.32
Q1-3.93
median-0.666
Q33.14
95-th percentile6
Maximum6.9309
Range15.0009
Interquartile range (IQR)7.07

Descriptive statistics

Standard deviation4.024872216
Coefficient of variation (CV)-8.061686982
Kurtosis-1.110784872
Mean-0.499259302
Median Absolute Deviation (MAD)3.466
Skewness0.1487683527
Sum-12624.27071
Variance16.19959635
MonotonicityNot monotonic
2022-08-25T18:31:49.332019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.9309506
 
2.0%
-1.1285
 
1.1%
-8.07254
 
1.0%
-0.3236
 
0.9%
-0.939154
 
0.6%
-0.619148
 
0.6%
-1.26146
 
0.6%
-2.79138
 
0.5%
-2.24136
 
0.5%
-3.34128
 
0.5%
Other values (1969)23155
91.6%
ValueCountFrequency (%)
-8.07254
1.0%
-8.043
 
< 0.1%
-8.034
 
< 0.1%
-8.022
 
< 0.1%
-8.012
 
< 0.1%
-81
 
< 0.1%
-7.994
 
< 0.1%
-7.982
 
< 0.1%
-7.977
 
< 0.1%
-7.961
 
< 0.1%
ValueCountFrequency (%)
6.9309506
2.0%
6.932
 
< 0.1%
6.924
 
< 0.1%
6.916
 
< 0.1%
6.96
 
< 0.1%
6.893
 
< 0.1%
6.8810
 
< 0.1%
6.874
 
< 0.1%
6.862
 
< 0.1%
6.855
 
< 0.1%

X1_DCBusVoltage
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct899
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06514739387
Minimum2.78 × 10-19
Maximum0.136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size197.7 KiB
2022-08-25T18:31:49.455545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.78 × 10-19
5-th percentile2.79 × 10-19
Q10.0415
median0.0668
Q30.0913
95-th percentile0.117
Maximum0.136
Range0.136
Interquartile range (IQR)0.0498

Descriptive statistics

Standard deviation0.03384449915
Coefficient of variation (CV)0.5195065703
Kurtosis-0.7191843205
Mean0.06514739387
Median Absolute Deviation (MAD)0.0248
Skewness-0.1008797805
Sum1647.317001
Variance0.001145450123
MonotonicityNot monotonic
2022-08-25T18:31:49.579587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.79 × 10-19770
 
3.0%
2.78 × 10-19709
 
2.8%
0.136516
 
2.0%
0.101259
 
1.0%
0.102251
 
1.0%
0.104235
 
0.9%
0.105203
 
0.8%
0.103198
 
0.8%
0.107191
 
0.8%
0.106190
 
0.8%
Other values (889)21764
86.1%
ValueCountFrequency (%)
2.78 × 10-19709
2.8%
2.79 × 10-19770
3.0%
1.43 × 10-61
 
< 0.1%
0.01291
 
< 0.1%
0.01381
 
< 0.1%
0.01423
 
< 0.1%
0.01442
 
< 0.1%
0.01452
 
< 0.1%
0.01462
 
< 0.1%
0.01472
 
< 0.1%
ValueCountFrequency (%)
0.136516
2.0%
0.13524
 
0.1%
0.13428
 
0.1%
0.13327
 
0.1%
0.13230
 
0.1%
0.13129
 
0.1%
0.1338
 
0.2%
0.12931
 
0.1%
0.12840
 
0.2%
0.12733
 
0.1%

X1_OutputCurrent
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean326.9470458
Minimum323
Maximum330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size197.7 KiB
2022-08-25T18:31:49.704359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum323
5-th percentile324
Q1326
median327
Q3327
95-th percentile330
Maximum330
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.447903496
Coefficient of variation (CV)0.004428556595
Kurtosis0.6533339751
Mean326.9470458
Median Absolute Deviation (MAD)0
Skewness0.004062296581
Sum8267183
Variance2.096424533
MonotonicityNot monotonic
2022-08-25T18:31:49.798957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
32713521
53.5%
3262874
 
11.4%
3282015
 
8.0%
3302000
 
7.9%
3251796
 
7.1%
3241482
 
5.9%
3291325
 
5.2%
323273
 
1.1%
ValueCountFrequency (%)
323273
 
1.1%
3241482
 
5.9%
3251796
 
7.1%
3262874
 
11.4%
32713521
53.5%
3282015
 
8.0%
3291325
 
5.2%
3302000
 
7.9%
ValueCountFrequency (%)
3302000
 
7.9%
3291325
 
5.2%
3282015
 
8.0%
32713521
53.5%
3262874
 
11.4%
3251796
 
7.1%
3241482
 
5.9%
323273
 
1.1%

X1_OutputVoltage
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1716
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.826651811
Minimum0
Maximum31.5
Zeros1480
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size197.7 KiB
2022-08-25T18:31:49.918175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.59
median7.14
Q310.2
95-th percentile24.8
Maximum31.5
Range31.5
Interquartile range (IQR)7.61

Descriptive statistics

Standard deviation6.851879094
Coefficient of variation (CV)0.8754546975
Kurtosis3.276320414
Mean7.826651811
Median Absolute Deviation (MAD)3.78
Skewness1.659808351
Sum197904.7177
Variance46.94824711
MonotonicityNot monotonic
2022-08-25T18:31:50.038803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01480
 
5.9%
31.5511
 
2.0%
10.2233
 
0.9%
10.3221
 
0.9%
10.1217
 
0.9%
10.4210
 
0.8%
10.5210
 
0.8%
10.6162
 
0.6%
10.8161
 
0.6%
10.7147
 
0.6%
Other values (1706)21734
86.0%
ValueCountFrequency (%)
01480
5.9%
0.02141
 
< 0.1%
0.03671
 
< 0.1%
0.06221
 
< 0.1%
0.06651
 
< 0.1%
0.07271
 
< 0.1%
0.07631
 
< 0.1%
0.08021
 
< 0.1%
0.08821
 
< 0.1%
0.09051
 
< 0.1%
ValueCountFrequency (%)
31.5511
2.0%
31.415
 
0.1%
31.311
 
< 0.1%
31.211
 
< 0.1%
31.119
 
0.1%
3120
 
0.1%
30.917
 
0.1%
30.816
 
0.1%
30.714
 
0.1%
30.610
 
< 0.1%

X1_OutputPower
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct4446
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0005482195522
Minimum-3.54 × 10-5
Maximum0.00501
Zeros2153
Zeros (%)8.5%
Negative4900
Negative (%)19.4%
Memory size197.7 KiB
2022-08-25T18:31:50.165503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-3.54 × 10-5
5-th percentile-2.1375 × 10-5
Q10
median0.000174
Q30.000585
95-th percentile0.0032275
Maximum0.00501
Range0.0050454
Interquartile range (IQR)0.000585

Descriptive statistics

Standard deviation0.001039271569
Coefficient of variation (CV)1.895721458
Kurtosis9.421809872
Mean0.0005482195522
Median Absolute Deviation (MAD)0.0001895
Skewness3.090292073
Sum13.8622796
Variance1.080085393 × 10-6
MonotonicityNot monotonic
2022-08-25T18:31:50.283370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02153
 
8.5%
0.00501509
 
2.0%
-3.54 × 10-5260
 
1.0%
-2.51 × 10-6106
 
0.4%
0.0011258
 
0.2%
0.001158
 
0.2%
0.0011857
 
0.2%
0.0010354
 
0.2%
0.0011752
 
0.2%
0.001251
 
0.2%
Other values (4436)21928
86.7%
ValueCountFrequency (%)
-3.54 × 10-5260
1.0%
-3.53 × 10-53
 
< 0.1%
-3.52 × 10-53
 
< 0.1%
-3.51 × 10-55
 
< 0.1%
-3.5 × 10-55
 
< 0.1%
-3.49 × 10-52
 
< 0.1%
-3.48 × 10-56
 
< 0.1%
-3.47 × 10-53
 
< 0.1%
-3.46 × 10-52
 
< 0.1%
-3.45 × 10-53
 
< 0.1%
ValueCountFrequency (%)
0.00501509
2.0%
0.0057
 
< 0.1%
0.004995
 
< 0.1%
0.004989
 
< 0.1%
0.004972
 
< 0.1%
0.004961
 
< 0.1%
0.004953
 
< 0.1%
0.004947
 
< 0.1%
0.004935
 
< 0.1%
0.0049211
 
< 0.1%

Y1_ActualPosition
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct335
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.23006407
Minimum72.4
Maximum158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size197.7 KiB
2022-08-25T18:31:50.423817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum72.4
5-th percentile72.4
Q177.5
median90
Q3105
95-th percentile158
Maximum158
Range85.6
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation29.2448804
Coefficient of variation (CV)0.2947179434
Kurtosis0.03237179815
Mean99.23006407
Median Absolute Deviation (MAD)13.3
Skewness1.199451273
Sum2509131.4
Variance855.2630297
MonotonicityNot monotonic
2022-08-25T18:31:50.536850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1584345
17.2%
77.82247
 
8.9%
1051853
 
7.3%
72.41513
 
6.0%
731430
 
5.7%
1031285
 
5.1%
89.51164
 
4.6%
91.61129
 
4.5%
74.5857
 
3.4%
102435
 
1.7%
Other values (325)9028
35.7%
ValueCountFrequency (%)
72.41513
6.0%
72.555
 
0.2%
72.642
 
0.2%
72.739
 
0.2%
72.828
 
0.1%
72.929
 
0.1%
731430
5.7%
73.173
 
0.3%
73.264
 
0.3%
73.349
 
0.2%
ValueCountFrequency (%)
1584345
17.2%
1579
 
< 0.1%
1567
 
< 0.1%
1552
 
< 0.1%
1548
 
< 0.1%
1539
 
< 0.1%
1523
 
< 0.1%
1517
 
< 0.1%
1508
 
< 0.1%
1492
 
< 0.1%

Y1_ActualVelocity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct692
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.6417976034
Minimum-32.3
Maximum7.2018
Zeros4987
Zeros (%)19.7%
Negative10093
Negative (%)39.9%
Memory size197.7 KiB
2022-08-25T18:31:50.667415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-32.3
5-th percentile-5.9
Q1-0.075
median0
Q30.1
95-th percentile4.95
Maximum7.2018
Range39.5018
Interquartile range (IQR)0.175

Descriptive statistics

Standard deviation5.25396804
Coefficient of variation (CV)-8.186331659
Kurtosis21.89710987
Mean-0.6417976034
Median Absolute Deviation (MAD)0.075
Skewness-4.204211272
Sum-16228.4942
Variance27.60418016
MonotonicityNot monotonic
2022-08-25T18:31:50.808403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04987
19.7%
-0.0252234
 
8.8%
0.0252070
 
8.2%
-0.051212
 
4.8%
0.051161
 
4.6%
0.075601
 
2.4%
-0.075558
 
2.2%
7.2018506
 
2.0%
-0.1495
 
2.0%
0.1468
 
1.9%
Other values (682)10994
43.5%
ValueCountFrequency (%)
-32.3319
1.3%
-32.260
 
0.2%
-32.130
 
0.1%
-3224
 
0.1%
-31.92
 
< 0.1%
-31.81
 
< 0.1%
-30.91
 
< 0.1%
-30.12
 
< 0.1%
-271
 
< 0.1%
-25.31
 
< 0.1%
ValueCountFrequency (%)
7.2018506
2.0%
7.21
 
< 0.1%
7.181
 
< 0.1%
71
 
< 0.1%
6.951
 
< 0.1%
6.931
 
< 0.1%
6.81
 
< 0.1%
6.721
 
< 0.1%
6.652
 
< 0.1%
6.632
 
< 0.1%

Y1_ActualAcceleration
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct41
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2416574389
Minimum-150
Maximum100
Zeros4571
Zeros (%)18.1%
Negative10374
Negative (%)41.0%
Memory size197.7 KiB
2022-08-25T18:31:50.930370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-150
5-th percentile-56.3
Q1-18.8
median0
Q318.8
95-th percentile62.5
Maximum100
Range250
Interquartile range (IQR)37.6

Descriptive statistics

Standard deviation36.90411764
Coefficient of variation (CV)-152.7125248
Kurtosis3.116705889
Mean-0.2416574389
Median Absolute Deviation (MAD)18.8
Skewness-0.4672082325
Sum-6110.55
Variance1361.913898
MonotonicityNot monotonic
2022-08-25T18:31:51.038219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
04571
18.1%
6.252041
 
8.1%
-12.52030
 
8.0%
-6.251960
 
7.8%
12.51854
 
7.3%
18.81346
 
5.3%
-18.81288
 
5.1%
-251180
 
4.7%
251025
 
4.1%
-31.3840
 
3.3%
Other values (31)7151
28.3%
ValueCountFrequency (%)
-150265
1.0%
-14414
 
0.1%
-13815
 
0.1%
-13119
 
0.1%
-12518
 
0.1%
-11915
 
0.1%
-11324
 
0.1%
-10622
 
0.1%
-10025
 
0.1%
-93.737
 
0.1%
ValueCountFrequency (%)
100519
2.1%
93.752
 
0.2%
87.555
 
0.2%
81.260
 
0.2%
75163
 
0.6%
68.7236
0.9%
62.5296
1.2%
56.3298
1.2%
50440
1.7%
43.8525
2.1%

Y1_CommandPosition
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct335
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.22627145
Minimum72.4
Maximum158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size197.7 KiB
2022-08-25T18:31:51.175137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum72.4
5-th percentile72.4
Q177.5
median90
Q3105
95-th percentile158
Maximum158
Range85.6
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation29.24280218
Coefficient of variation (CV)0.2947082637
Kurtosis0.03318740881
Mean99.22627145
Median Absolute Deviation (MAD)13.3
Skewness1.199722284
Sum2509035.5
Variance855.1414791
MonotonicityNot monotonic
2022-08-25T18:31:51.336802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1584344
17.2%
77.82246
 
8.9%
1051851
 
7.3%
72.41512
 
6.0%
731430
 
5.7%
1031285
 
5.1%
89.51168
 
4.6%
91.61122
 
4.4%
74.5853
 
3.4%
102433
 
1.7%
Other values (325)9042
35.8%
ValueCountFrequency (%)
72.41512
6.0%
72.555
 
0.2%
72.642
 
0.2%
72.740
 
0.2%
72.829
 
0.1%
72.927
 
0.1%
731430
5.7%
73.176
 
0.3%
73.262
 
0.2%
73.352
 
0.2%
ValueCountFrequency (%)
1584344
17.2%
1579
 
< 0.1%
1568
 
< 0.1%
1552
 
< 0.1%
1546
 
< 0.1%
15310
 
< 0.1%
1524
 
< 0.1%
1514
 
< 0.1%
1508
 
< 0.1%
1495
 
< 0.1%

Y1_CommandVelocity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1655
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.6226088918
Minimum-32.4
Maximum7.7027
Zeros14647
Zeros (%)57.9%
Negative5179
Negative (%)20.5%
Memory size197.7 KiB
2022-08-25T18:31:51.471617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-32.4
5-th percentile-6
Q10
median0
Q30
95-th percentile5.145
Maximum7.7027
Range40.1027
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.27903344
Coefficient of variation (CV)-8.478891822
Kurtosis21.87893863
Mean-0.6226088918
Median Absolute Deviation (MAD)0
Skewness-4.176726445
Sum-15743.28844
Variance27.86819407
MonotonicityNot monotonic
2022-08-25T18:31:51.631758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
014647
57.9%
31291
 
5.1%
-31279
 
5.1%
7.7027506
 
2.0%
6452
 
1.8%
-6424
 
1.7%
-32.4387
 
1.5%
-2082
 
0.3%
2.9956
 
0.2%
-1252
 
0.2%
Other values (1645)6110
24.2%
ValueCountFrequency (%)
-32.4387
1.5%
-32.350
 
0.2%
-32.11
 
< 0.1%
-29.81
 
< 0.1%
-28.51
 
< 0.1%
-25.91
 
< 0.1%
-24.61
 
< 0.1%
-22.450
 
0.2%
-22.12
 
< 0.1%
-21.81
 
< 0.1%
ValueCountFrequency (%)
7.7027506
2.0%
7.73
 
< 0.1%
7.511
 
< 0.1%
7.411
 
< 0.1%
7.261
 
< 0.1%
7.181
 
< 0.1%
7.171
 
< 0.1%
7.131
 
< 0.1%
7.121
 
< 0.1%
7.071
 
< 0.1%

Y1_CommandAcceleration
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct1853
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.4418407981
Minimum-58.1135
Maximum15.1
Zeros18407
Zeros (%)72.8%
Negative3076
Negative (%)12.2%
Memory size197.7 KiB
2022-08-25T18:31:51.773266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-58.1135
5-th percentile-2.78
Q10
median0
Q30
95-th percentile3.37
Maximum15.1
Range73.2135
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.200316834
Coefficient of variation (CV)-16.29617922
Kurtosis46.03124476
Mean-0.4418407981
Median Absolute Deviation (MAD)0
Skewness-6.024634294
Sum-11172.38642
Variance51.84456251
MonotonicityNot monotonic
2022-08-25T18:31:51.894238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
018407
72.8%
15.1507
 
2.0%
-58.1135253
 
1.0%
-9.54 × 10-5132
 
0.5%
9.54 × 10-5127
 
0.5%
-0.00019146
 
0.2%
-2.2640
 
0.2%
0.00019139
 
0.2%
2.2639
 
0.2%
2.2737
 
0.1%
Other values (1843)5659
 
22.4%
ValueCountFrequency (%)
-58.1135253
1.0%
-58.11
 
< 0.1%
-581
 
< 0.1%
-56.61
 
< 0.1%
-56.52
 
< 0.1%
-56.31
 
< 0.1%
-55.61
 
< 0.1%
-55.31
 
< 0.1%
-54.91
 
< 0.1%
-54.82
 
< 0.1%
ValueCountFrequency (%)
15.1507
2.0%
152
 
< 0.1%
14.62
 
< 0.1%
14.42
 
< 0.1%
14.31
 
< 0.1%
14.13
 
< 0.1%
13.712
 
< 0.1%
13.610
 
< 0.1%
13.54
 
< 0.1%
13.410
 
< 0.1%

Y1_CurrentFeedback
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2748
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0866307416
Minimum-10.4
Maximum8.2209
Zeros0
Zeros (%)0.0%
Negative12011
Negative (%)47.5%
Memory size197.7 KiB
2022-08-25T18:31:52.026609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-10.4
5-th percentile-7.57
Q1-3.09
median0.146
Q32.9
95-th percentile6.8
Maximum8.2209
Range18.6209
Interquartile range (IQR)5.99

Descriptive statistics

Standard deviation4.275364035
Coefficient of variation (CV)-49.3515807
Kurtosis-0.4729860479
Mean-0.0866307416
Median Absolute Deviation (MAD)3.014
Skewness-0.1897100155
Sum-2190.544932
Variance18.27873763
MonotonicityNot monotonic
2022-08-25T18:31:52.150075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.2209506
 
2.0%
-10.4267
 
1.1%
-1.33136
 
0.5%
-1.06117
 
0.5%
0.714106
 
0.4%
1.15106
 
0.4%
1.01106
 
0.4%
1.39102
 
0.4%
1.4693
 
0.4%
0.3891
 
0.4%
Other values (2738)23656
93.6%
ValueCountFrequency (%)
-10.4267
1.1%
-10.313
 
0.1%
-10.212
 
< 0.1%
-10.115
 
0.1%
-105
 
< 0.1%
-9.994
 
< 0.1%
-9.981
 
< 0.1%
-9.963
 
< 0.1%
-9.941
 
< 0.1%
-9.931
 
< 0.1%
ValueCountFrequency (%)
8.2209506
2.0%
8.221
 
< 0.1%
8.213
 
< 0.1%
8.27
 
< 0.1%
8.195
 
< 0.1%
8.189
 
< 0.1%
8.174
 
< 0.1%
8.163
 
< 0.1%
8.154
 
< 0.1%
8.143
 
< 0.1%

Y1_DCBusVoltage
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct963
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06328877248
Minimum2.68 × 10-19
Maximum0.176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size197.7 KiB
2022-08-25T18:31:52.289334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.68 × 10-19
5-th percentile2.78 × 10-19
Q10.0219
median0.0578
Q30.095575
95-th percentile0.147
Maximum0.176
Range0.176
Interquartile range (IQR)0.073675

Descriptive statistics

Standard deviation0.04540659954
Coefficient of variation (CV)0.7174511016
Kurtosis-0.6160031904
Mean0.06328877248
Median Absolute Deviation (MAD)0.0364
Skewness0.5570569687
Sum1600.319901
Variance0.002061759282
MonotonicityNot monotonic
2022-08-25T18:31:52.437353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.68 × 10-19770
 
3.0%
0.176510
 
2.0%
2.69 × 10-19385
 
1.5%
2.78 × 10-19324
 
1.3%
0.0181188
 
0.7%
0.0159169
 
0.7%
0.101160
 
0.6%
0.102145
 
0.6%
0.106132
 
0.5%
0.108130
 
0.5%
Other values (953)22373
88.5%
ValueCountFrequency (%)
2.68 × 10-19770
3.0%
2.69 × 10-19385
1.5%
2.78 × 10-19324
1.3%
1.01 × 10-61
 
< 0.1%
0.01081
 
< 0.1%
0.01141
 
< 0.1%
0.01171
 
< 0.1%
0.01181
 
< 0.1%
0.0122
 
< 0.1%
0.01232
 
< 0.1%
ValueCountFrequency (%)
0.176510
2.0%
0.17518
 
0.1%
0.17415
 
0.1%
0.1738
 
< 0.1%
0.17213
 
0.1%
0.17119
 
0.1%
0.1718
 
0.1%
0.16916
 
0.1%
0.16814
 
0.1%
0.16717
 
0.1%

Y1_OutputCurrent
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean325.8617812
Minimum322
Maximum332
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size197.7 KiB
2022-08-25T18:31:52.553358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum322
5-th percentile323
Q1325
median326
Q3326
95-th percentile329
Maximum332
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.790952647
Coefficient of variation (CV)0.005496050014
Kurtosis2.659465981
Mean325.8617812
Median Absolute Deviation (MAD)1
Skewness1.080605349
Sum8239741
Variance3.207511385
MonotonicityNot monotonic
2022-08-25T18:31:52.689284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
32610790
42.7%
3255897
23.3%
3241845
 
7.3%
3281791
 
7.1%
3231726
 
6.8%
3291233
 
4.9%
327859
 
3.4%
332764
 
3.0%
322373
 
1.5%
3316
 
< 0.1%
ValueCountFrequency (%)
322373
 
1.5%
3231726
 
6.8%
3241845
 
7.3%
3255897
23.3%
32610790
42.7%
327859
 
3.4%
3281791
 
7.1%
3291233
 
4.9%
3302
 
< 0.1%
3316
 
< 0.1%
ValueCountFrequency (%)
332764
 
3.0%
3316
 
< 0.1%
3302
 
< 0.1%
3291233
 
4.9%
3281791
 
7.1%
327859
 
3.4%
32610790
42.7%
3255897
23.3%
3241845
 
7.3%
3231726
 
6.8%

Y1_OutputVoltage
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1945
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.78561657
Minimum0
Maximum36.809
Zeros1480
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size197.7 KiB
2022-08-25T18:31:52.839020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.81
median4.93
Q39.61
95-th percentile20.5
Maximum36.809
Range36.809
Interquartile range (IQR)7.8

Descriptive statistics

Standard deviation7.214526083
Coefficient of variation (CV)1.063208628
Kurtosis6.235204167
Mean6.78561657
Median Absolute Deviation (MAD)3.56
Skewness2.262556453
Sum171581.1006
Variance52.0493866
MonotonicityNot monotonic
2022-08-25T18:31:52.970476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01480
 
5.9%
36.809506
 
2.0%
10.3166
 
0.7%
10.1147
 
0.6%
10.4147
 
0.6%
10.2144
 
0.6%
10.5140
 
0.6%
10.7139
 
0.5%
2.43137
 
0.5%
2.62134
 
0.5%
Other values (1935)22146
87.6%
ValueCountFrequency (%)
01480
5.9%
0.01011
 
< 0.1%
0.03891
 
< 0.1%
0.04711
 
< 0.1%
0.05181
 
< 0.1%
0.05241
 
< 0.1%
0.06141
 
< 0.1%
0.06871
 
< 0.1%
0.06911
 
< 0.1%
0.06921
 
< 0.1%
ValueCountFrequency (%)
36.809506
2.0%
36.83
 
< 0.1%
36.72
 
< 0.1%
36.61
 
< 0.1%
36.53
 
< 0.1%
36.41
 
< 0.1%
36.31
 
< 0.1%
36.25
 
< 0.1%
364
 
< 0.1%
35.97
 
< 0.1%

Y1_OutputPower
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct5413
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0005643621387
Minimum-3.97 × 10-5
Maximum0.0093909
Zeros3697
Zeros (%)14.6%
Negative5570
Negative (%)22.0%
Memory size197.7 KiB
2022-08-25T18:31:53.110524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-3.97 × 10-5
5-th percentile-1.33 × 10-5
Q10
median4.43 × 10-6
Q30.000506
95-th percentile0.00234
Maximum0.0093909
Range0.0094306
Interquartile range (IQR)0.000506

Descriptive statistics

Standard deviation0.001573672203
Coefficient of variation (CV)2.788408533
Kurtosis20.52501191
Mean0.0005643621387
Median Absolute Deviation (MAD)1.643 × 10-5
Skewness4.465413438
Sum14.27046104
Variance2.476444203 × 10-6
MonotonicityNot monotonic
2022-08-25T18:31:53.240017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03697
 
14.6%
0.0093909506
 
2.0%
-3.97 × 10-5254
 
1.0%
-8.42 × 10-7113
 
0.4%
5.71 × 10-7109
 
0.4%
0.0010131
 
0.1%
0.0012126
 
0.1%
0.0011226
 
0.1%
0.0011525
 
0.1%
0.0011824
 
0.1%
Other values (5403)20475
81.0%
ValueCountFrequency (%)
-3.97 × 10-5254
1.0%
-3.96 × 10-51
 
< 0.1%
-3.95 × 10-54
 
< 0.1%
-3.94 × 10-51
 
< 0.1%
-3.93 × 10-51
 
< 0.1%
-3.92 × 10-51
 
< 0.1%
-3.91 × 10-53
 
< 0.1%
-3.9 × 10-51
 
< 0.1%
-3.89 × 10-55
 
< 0.1%
-3.88 × 10-53
 
< 0.1%
ValueCountFrequency (%)
0.0093909506
2.0%
0.009391
 
< 0.1%
0.00932
 
< 0.1%
0.009261
 
< 0.1%
0.009221
 
< 0.1%
0.009171
 
< 0.1%
0.009131
 
< 0.1%
0.009071
 
< 0.1%
0.009061
 
< 0.1%
0.008991
 
< 0.1%

Z1_ActualPosition
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct474
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.78063751
Minimum27.5
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size197.7 KiB
2022-08-25T18:31:53.369356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum27.5
5-th percentile27.5
Q128.5
median29.5
Q355.5
95-th percentile119
Maximum119
Range91.5
Interquartile range (IQR)27

Descriptive statistics

Standard deviation34.25564967
Coefficient of variation (CV)0.716935802
Kurtosis0.3800751201
Mean47.78063751
Median Absolute Deviation (MAD)1.8
Skewness1.477857175
Sum1208181.2
Variance1173.449535
MonotonicityNot monotonic
2022-08-25T18:31:53.512467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1194397
17.4%
29.73149
12.5%
28.73036
12.0%
27.72977
11.8%
29.52852
11.3%
27.52631
10.4%
28.52629
10.4%
55.71377
 
5.4%
55.5541
 
2.1%
54.740
 
0.2%
Other values (464)1657
 
6.6%
ValueCountFrequency (%)
27.52631
10.4%
27.63
 
< 0.1%
27.72977
11.8%
27.86
 
< 0.1%
27.94
 
< 0.1%
282
 
< 0.1%
28.14
 
< 0.1%
28.22
 
< 0.1%
28.34
 
< 0.1%
28.42
 
< 0.1%
ValueCountFrequency (%)
1194397
17.4%
1189
 
< 0.1%
1178
 
< 0.1%
1162
 
< 0.1%
1158
 
< 0.1%
1148
 
< 0.1%
1132
 
< 0.1%
1125
 
< 0.1%
1119
 
< 0.1%
1105
 
< 0.1%

Z1_ActualVelocity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct159
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.8640401803
Minimum-33.7
Maximum0.05
Zeros13878
Zeros (%)54.9%
Negative6208
Negative (%)24.6%
Memory size197.7 KiB
2022-08-25T18:31:53.663165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-33.7
5-th percentile-0.125
Q10
median0
Q30
95-th percentile0.025
Maximum0.05
Range33.75
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.956594005
Coefficient of variation (CV)-5.736531839
Kurtosis37.69805749
Mean-0.8640401803
Median Absolute Deviation (MAD)0
Skewness-6.22662173
Sum-21848.12
Variance24.56782413
MonotonicityNot monotonic
2022-08-25T18:31:53.789952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013878
54.9%
-0.0254643
 
18.4%
0.0254625
 
18.3%
0.05575
 
2.3%
-33.7301
 
1.2%
-0.05274
 
1.1%
-33.6115
 
0.5%
-33.599
 
0.4%
-2.935
 
0.1%
-2.9234
 
0.1%
Other values (149)707
 
2.8%
ValueCountFrequency (%)
-33.7301
1.2%
-33.6115
 
0.5%
-33.599
 
0.4%
-33.48
 
< 0.1%
-33.31
 
< 0.1%
-32.81
 
< 0.1%
-32.51
 
< 0.1%
-32.32
 
< 0.1%
-311
 
< 0.1%
-30.91
 
< 0.1%
ValueCountFrequency (%)
0.05575
 
2.3%
0.0254625
 
18.3%
013878
54.9%
-0.0254643
 
18.4%
-0.05274
 
1.1%
-0.07518
 
0.1%
-0.16
 
< 0.1%
-0.1253
 
< 0.1%
-0.151
 
< 0.1%
-0.21
 
< 0.1%

Z1_ActualAcceleration
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.3635766827
Minimum-43.8
Maximum25
Zeros10935
Zeros (%)43.2%
Negative7329
Negative (%)29.0%
Memory size197.7 KiB
2022-08-25T18:31:53.895310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-43.8
5-th percentile-12.5
Q1-6.25
median0
Q36.25
95-th percentile12.5
Maximum25
Range68.8
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation10.1587117
Coefficient of variation (CV)-27.94104292
Kurtosis3.356944799
Mean-0.3635766827
Median Absolute Deviation (MAD)6.25
Skewness-0.7590533877
Sum-9193.4
Variance103.1994233
MonotonicityNot monotonic
2022-08-25T18:31:53.992402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
010935
43.2%
-6.253314
 
13.1%
12.53128
 
12.4%
-12.53059
 
12.1%
6.253020
 
11.9%
25573
 
2.3%
-18.8330
 
1.3%
18.8301
 
1.2%
-43.8271
 
1.1%
-25221
 
0.9%
Other values (2)134
 
0.5%
ValueCountFrequency (%)
-43.8271
 
1.1%
-37.566
 
0.3%
-31.368
 
0.3%
-25221
 
0.9%
-18.8330
 
1.3%
-12.53059
 
12.1%
-6.253314
 
13.1%
010935
43.2%
6.253020
 
11.9%
12.53128
 
12.4%
ValueCountFrequency (%)
25573
 
2.3%
18.8301
 
1.2%
12.53128
 
12.4%
6.253020
 
11.9%
010935
43.2%
-6.253314
 
13.1%
-12.53059
 
12.1%
-18.8330
 
1.3%
-25221
 
0.9%
-31.368
 
0.3%

Z1_CommandPosition
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct484
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.77803132
Minimum27.5
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size197.7 KiB
2022-08-25T18:31:54.111425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum27.5
5-th percentile27.5
Q128.5
median29.5
Q355.5
95-th percentile119
Maximum119
Range91.5
Interquartile range (IQR)27

Descriptive statistics

Standard deviation34.25251746
Coefficient of variation (CV)0.7169093516
Kurtosis0.3809266356
Mean47.77803132
Median Absolute Deviation (MAD)1.8
Skewness1.478096151
Sum1208115.3
Variance1173.234952
MonotonicityNot monotonic
2022-08-25T18:31:54.230509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1194397
17.4%
29.73148
12.4%
28.73034
12.0%
27.72980
11.8%
29.52852
11.3%
27.52632
10.4%
28.52629
10.4%
55.71378
 
5.4%
55.5539
 
2.1%
30.743
 
0.2%
Other values (474)1654
 
6.5%
ValueCountFrequency (%)
27.52632
10.4%
27.61
 
< 0.1%
27.72980
11.8%
27.85
 
< 0.1%
27.92
 
< 0.1%
285
 
< 0.1%
28.11
 
< 0.1%
28.23
 
< 0.1%
28.35
 
< 0.1%
28.43
 
< 0.1%
ValueCountFrequency (%)
1194397
17.4%
1187
 
< 0.1%
1179
 
< 0.1%
1162
 
< 0.1%
1156
 
< 0.1%
1148
 
< 0.1%
1136
 
< 0.1%
1123
 
< 0.1%
1118
 
< 0.1%
1107
 
< 0.1%

Z1_CommandVelocity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct44
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.863270189
Minimum-33.7
Maximum0
Zeros24031
Zeros (%)95.0%
Negative1255
Negative (%)5.0%
Memory size197.7 KiB
2022-08-25T18:31:54.368758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-33.7
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range33.7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.956576434
Coefficient of variation (CV)-5.741628168
Kurtosis37.78133772
Mean-0.863270189
Median Absolute Deviation (MAD)0
Skewness-6.234334757
Sum-21828.65
Variance24.56764995
MonotonicityNot monotonic
2022-08-25T18:31:54.496200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
024031
95.0%
-3457
 
1.8%
-33.7267
 
1.1%
-33.6258
 
1.0%
-6154
 
0.6%
-2035
 
0.1%
-1221
 
0.1%
-1515
 
0.1%
-0.54
 
< 0.1%
-304
 
< 0.1%
Other values (34)40
 
0.2%
ValueCountFrequency (%)
-33.7267
1.1%
-33.6258
1.0%
-33.31
 
< 0.1%
-321
 
< 0.1%
-311
 
< 0.1%
-304
 
< 0.1%
-29.61
 
< 0.1%
-281
 
< 0.1%
-271
 
< 0.1%
-261
 
< 0.1%
ValueCountFrequency (%)
024031
95.0%
-0.54
 
< 0.1%
-1.883
 
< 0.1%
-21
 
< 0.1%
-2.671
 
< 0.1%
-2.781
 
< 0.1%
-3457
 
1.8%
-41
 
< 0.1%
-4.031
 
< 0.1%
-4.381
 
< 0.1%

Z1_CommandAcceleration
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0.0
25286 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters75858
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.025286
100.0%

Length

2022-08-25T18:31:54.637092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-25T18:31:54.754958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.025286
100.0%

Most occurring characters

ValueCountFrequency (%)
050572
66.7%
.25286
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number50572
66.7%
Other Punctuation25286
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
050572
100.0%
Other Punctuation
ValueCountFrequency (%)
.25286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common75858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
050572
66.7%
.25286
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII75858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
050572
66.7%
.25286
33.3%

Z1_CurrentFeedback
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0.0
25286 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters75858
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.025286
100.0%

Length

2022-08-25T18:31:54.858992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-25T18:31:54.964079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.025286
100.0%

Most occurring characters

ValueCountFrequency (%)
050572
66.7%
.25286
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number50572
66.7%
Other Punctuation25286
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
050572
100.0%
Other Punctuation
ValueCountFrequency (%)
.25286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common75858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
050572
66.7%
.25286
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII75858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
050572
66.7%
.25286
33.3%

Z1_DCBusVoltage
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0.0
25286 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters75858
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.025286
100.0%

Length

2022-08-25T18:31:56.736128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-25T18:31:56.819078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.025286
100.0%

Most occurring characters

ValueCountFrequency (%)
050572
66.7%
.25286
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number50572
66.7%
Other Punctuation25286
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
050572
100.0%
Other Punctuation
ValueCountFrequency (%)
.25286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common75858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
050572
66.7%
.25286
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII75858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
050572
66.7%
.25286
33.3%

Z1_OutputCurrent
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0.0
25286 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters75858
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.025286
100.0%

Length

2022-08-25T18:31:56.910355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-25T18:31:57.011081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.025286
100.0%

Most occurring characters

ValueCountFrequency (%)
050572
66.7%
.25286
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number50572
66.7%
Other Punctuation25286
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
050572
100.0%
Other Punctuation
ValueCountFrequency (%)
.25286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common75858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
050572
66.7%
.25286
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII75858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
050572
66.7%
.25286
33.3%

Z1_OutputVoltage
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0.0
25286 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters75858
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.025286
100.0%

Length

2022-08-25T18:31:57.098713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-25T18:31:57.185040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.025286
100.0%

Most occurring characters

ValueCountFrequency (%)
050572
66.7%
.25286
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number50572
66.7%
Other Punctuation25286
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
050572
100.0%
Other Punctuation
ValueCountFrequency (%)
.25286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common75858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
050572
66.7%
.25286
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII75858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
050572
66.7%
.25286
33.3%

S1_ActualPosition
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2789
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-115.8029646
Minimum-2100
Maximum2110
Zeros0
Zeros (%)0.0%
Negative13149
Negative (%)52.0%
Memory size197.7 KiB
2022-08-25T18:31:57.283839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-2100
5-th percentile-2000
Q1-1160
median-112.5
Q3860.75
95-th percentile1930
Maximum2110
Range4210
Interquartile range (IQR)2020.75

Descriptive statistics

Standard deviation1211.021833
Coefficient of variation (CV)-10.45760649
Kurtosis-1.074841695
Mean-115.8029646
Median Absolute Deviation (MAD)1017.5
Skewness0.1176981611
Sum-2928193.762
Variance1466573.881
MonotonicityNot monotonic
2022-08-25T18:31:57.421205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1160813
 
3.2%
-978619
 
2.4%
339554
 
2.2%
2110542
 
2.1%
383391
 
1.5%
905391
 
1.5%
-273375
 
1.5%
-2060354
 
1.4%
-1510336
 
1.3%
-2050318
 
1.3%
Other values (2779)20593
81.4%
ValueCountFrequency (%)
-2100276
1.1%
-209040
 
0.2%
-208040
 
0.2%
-207044
 
0.2%
-2060354
1.4%
-2050318
1.3%
-204041
 
0.2%
-203040
 
0.2%
-202038
 
0.2%
-201040
 
0.2%
ValueCountFrequency (%)
2110542
2.1%
210043
 
0.2%
209041
 
0.2%
208039
 
0.2%
207042
 
0.2%
206042
 
0.2%
205039
 
0.2%
204042
 
0.2%
203041
 
0.2%
202043
 
0.2%

S1_ActualVelocity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct351
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.98623468
Minimum-0.002
Maximum53.5
Zeros2142
Zeros (%)8.5%
Negative2231
Negative (%)8.8%
Memory size197.7 KiB
2022-08-25T18:31:57.578976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.002
5-th percentile-0.001
Q10.002
median53.3
Q353.4
95-th percentile53.5
Maximum53.5
Range53.502
Interquartile range (IQR)53.398

Descriptive statistics

Standard deviation23.49112094
Coefficient of variation (CV)0.6025491081
Kurtosis-0.9020099894
Mean38.98623468
Median Absolute Deviation (MAD)0.1
Skewness-1.041210644
Sum985805.9302
Variance551.8327629
MonotonicityNot monotonic
2022-08-25T18:31:57.732371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.37749
30.6%
53.46515
25.8%
53.22519
 
10.0%
02142
 
8.5%
53.51352
 
5.3%
0.0011185
 
4.7%
-0.0011158
 
4.6%
-0.002446
 
1.8%
0.00075311
 
1.2%
-0.00175304
 
1.2%
Other values (341)1605
 
6.3%
ValueCountFrequency (%)
-0.002446
 
1.8%
-0.00175304
 
1.2%
-0.00151
 
< 0.1%
-0.0012528
 
0.1%
-0.0011158
4.6%
-0.00075281
 
1.1%
-0.00052
 
< 0.1%
-0.0002511
 
< 0.1%
02142
8.5%
0.0002516
 
0.1%
ValueCountFrequency (%)
53.51352
 
5.3%
53.46515
25.8%
53.37749
30.6%
53.22519
 
10.0%
53.1153
 
0.6%
531
 
< 0.1%
52.71
 
< 0.1%
52.42
 
< 0.1%
52.31
 
< 0.1%
52.21
 
< 0.1%

S1_ActualAcceleration
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct1576
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04878219173
Minimum-80.1
Maximum66.309
Zeros1007
Zeros (%)4.0%
Negative12313
Negative (%)48.7%
Memory size197.7 KiB
2022-08-25T18:31:57.885393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-80.1
5-th percentile-57.4
Q1-15.9
median0
Q317.5
95-th percentile55.8
Maximum66.309
Range146.409
Interquartile range (IQR)33.4

Descriptive statistics

Standard deviation31.18415952
Coefficient of variation (CV)639.2529409
Kurtosis0.1204333212
Mean0.04878219173
Median Absolute Deviation (MAD)16.8
Skewness-0.1300997357
Sum1233.5065
Variance972.4518051
MonotonicityNot monotonic
2022-08-25T18:31:58.072422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01007
 
4.0%
-0.25957
 
3.8%
0.25793
 
3.1%
0.5580
 
2.3%
-0.5549
 
2.2%
66.309506
 
2.0%
-80.1254
 
1.0%
0.188244
 
1.0%
0.75244
 
1.0%
-0.75220
 
0.9%
Other values (1566)19932
78.8%
ValueCountFrequency (%)
-80.1254
1.0%
-803
 
< 0.1%
-79.95
 
< 0.1%
-79.72
 
< 0.1%
-79.61
 
< 0.1%
-79.52
 
< 0.1%
-79.31
 
< 0.1%
-79.23
 
< 0.1%
-79.17
 
< 0.1%
-791
 
< 0.1%
ValueCountFrequency (%)
66.309506
2.0%
66.34
 
< 0.1%
66.25
 
< 0.1%
66.15
 
< 0.1%
661
 
< 0.1%
65.911
 
< 0.1%
65.82
 
< 0.1%
65.76
 
< 0.1%
65.611
 
< 0.1%
65.52
 
< 0.1%

S1_CommandPosition
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2770
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-115.4869544
Minimum-2100
Maximum2110
Zeros0
Zeros (%)0.0%
Negative13147
Negative (%)52.0%
Memory size197.7 KiB
2022-08-25T18:31:58.353693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-2100
5-th percentile-2000
Q1-1160
median-112
Q3861.75
95-th percentile1930
Maximum2110
Range4210
Interquartile range (IQR)2021.75

Descriptive statistics

Standard deviation1211.060899
Coefficient of variation (CV)-10.48656019
Kurtosis-1.074875663
Mean-115.4869544
Median Absolute Deviation (MAD)1017
Skewness0.1176830005
Sum-2920203.13
Variance1466668.501
MonotonicityNot monotonic
2022-08-25T18:31:58.603146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1160813
 
3.2%
-978620
 
2.5%
339550
 
2.2%
2110544
 
2.2%
383391
 
1.5%
905389
 
1.5%
-273373
 
1.5%
-2060354
 
1.4%
-1510335
 
1.3%
-2050317
 
1.3%
Other values (2760)20600
81.5%
ValueCountFrequency (%)
-2100275
1.1%
-209038
 
0.2%
-208042
 
0.2%
-207044
 
0.2%
-2060354
1.4%
-2050317
1.3%
-204041
 
0.2%
-203040
 
0.2%
-202039
 
0.2%
-201038
 
0.2%
ValueCountFrequency (%)
2110544
2.2%
210042
 
0.2%
209041
 
0.2%
208042
 
0.2%
207040
 
0.2%
206043
 
0.2%
205037
 
0.1%
204042
 
0.2%
203042
 
0.2%
202042
 
0.2%

S1_CommandVelocity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct75
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.65001186
Minimum0
Maximum53.3
Zeros6902
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size197.7 KiB
2022-08-25T18:31:58.793582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median53.3
Q353.3
95-th percentile53.3
Maximum53.3
Range53.3
Interquartile range (IQR)53.3

Descriptive statistics

Standard deviation23.75808978
Coefficient of variation (CV)0.6146981238
Kurtosis-0.9794845415
Mean38.65001186
Median Absolute Deviation (MAD)0
Skewness-1.008662462
Sum977304.2
Variance564.4468299
MonotonicityNot monotonic
2022-08-25T18:31:59.017985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.318286
72.3%
06902
 
27.3%
11.83
 
< 0.1%
32.23
 
< 0.1%
393
 
< 0.1%
28.62
 
< 0.1%
52.22
 
< 0.1%
29.82
 
< 0.1%
39.82
 
< 0.1%
49.82
 
< 0.1%
Other values (65)79
 
0.3%
ValueCountFrequency (%)
06902
27.3%
0.21
 
< 0.1%
11
 
< 0.1%
1.82
 
< 0.1%
2.22
 
< 0.1%
2.61
 
< 0.1%
31
 
< 0.1%
4.21
 
< 0.1%
51
 
< 0.1%
6.62
 
< 0.1%
ValueCountFrequency (%)
53.318286
72.3%
531
 
< 0.1%
52.22
 
< 0.1%
51.82
 
< 0.1%
51.41
 
< 0.1%
511
 
< 0.1%
50.21
 
< 0.1%
49.82
 
< 0.1%
49.41
 
< 0.1%
48.62
 
< 0.1%

S1_CommandAcceleration
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0.0
18335 
9.54e-07
3576 
-9.54e-07
3375 

Length

Max length9
Median length3
Mean length4.507949063
Min length3

Characters and Unicode

Total characters113988
Distinct characters8
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.018335
72.5%
9.54e-073576
 
14.1%
-9.54e-073375
 
13.3%

Length

2022-08-25T18:31:59.239154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-25T18:31:59.371990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.018335
72.5%
9.54e-076951
 
27.5%

Most occurring characters

ValueCountFrequency (%)
043621
38.3%
.25286
22.2%
-10326
 
9.1%
96951
 
6.1%
56951
 
6.1%
46951
 
6.1%
e6951
 
6.1%
76951
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number71425
62.7%
Other Punctuation25286
 
22.2%
Dash Punctuation10326
 
9.1%
Lowercase Letter6951
 
6.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
043621
61.1%
96951
 
9.7%
56951
 
9.7%
46951
 
9.7%
76951
 
9.7%
Other Punctuation
ValueCountFrequency (%)
.25286
100.0%
Dash Punctuation
ValueCountFrequency (%)
-10326
100.0%
Lowercase Letter
ValueCountFrequency (%)
e6951
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common107037
93.9%
Latin6951
 
6.1%

Most frequent character per script

Common
ValueCountFrequency (%)
043621
40.8%
.25286
23.6%
-10326
 
9.6%
96951
 
6.5%
56951
 
6.5%
46951
 
6.5%
76951
 
6.5%
Latin
ValueCountFrequency (%)
e6951
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII113988
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
043621
38.3%
.25286
22.2%
-10326
 
9.1%
96951
 
6.1%
56951
 
6.1%
46951
 
6.1%
e6951
 
6.1%
76951
 
6.1%

S1_CurrentFeedback
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1121
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.09728611
Minimum-1.8
Maximum28
Zeros0
Zeros (%)0.0%
Negative2879
Negative (%)11.4%
Memory size197.7 KiB
2022-08-25T18:31:59.518673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-1.8
5-th percentile-0.685
Q10.821
median18.8
Q322.3
95-th percentile26.5
Maximum28
Range29.8
Interquartile range (IQR)21.479

Descriptive statistics

Standard deviation9.781844685
Coefficient of variation (CV)0.6479207332
Kurtosis-1.094585371
Mean15.09728611
Median Absolute Deviation (MAD)4.4
Skewness-0.6974443968
Sum381749.9766
Variance95.68448545
MonotonicityNot monotonic
2022-08-25T18:31:59.696681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28529
 
2.1%
-1.8262
 
1.0%
21.2209
 
0.8%
20.8202
 
0.8%
21.7201
 
0.8%
19199
 
0.8%
20.7198
 
0.8%
19.4197
 
0.8%
20.5195
 
0.8%
20.2194
 
0.8%
Other values (1111)22900
90.6%
ValueCountFrequency (%)
-1.8262
1.0%
-1.791
 
< 0.1%
-1.749
 
< 0.1%
-1.732
 
< 0.1%
-1.722
 
< 0.1%
-1.714
 
< 0.1%
-1.681
 
< 0.1%
-1.641
 
< 0.1%
-1.623
 
< 0.1%
-1.61
 
< 0.1%
ValueCountFrequency (%)
28529
2.1%
27.931
 
0.1%
27.828
 
0.1%
27.727
 
0.1%
27.647
 
0.2%
27.543
 
0.2%
27.455
 
0.2%
27.343
 
0.2%
27.245
 
0.2%
27.146
 
0.2%

S1_DCBusVoltage
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct405
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6626324258
Minimum0
Maximum1.11
Zeros770
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size197.7 KiB
2022-08-25T18:31:59.834031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.7 × 10-19
Q12.79 × 10-19
median0.858
Q30.952
95-th percentile1.07
Maximum1.11
Range1.11
Interquartile range (IQR)0.952

Descriptive statistics

Standard deviation0.4145472736
Coefficient of variation (CV)0.6256066824
Kurtosis-1.030501764
Mean0.6626324258
Median Absolute Deviation (MAD)0.116
Skewness-0.883236803
Sum16755.32352
Variance0.171849442
MonotonicityNot monotonic
2022-08-25T18:31:59.957826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.71 × 10-191300
 
5.1%
2.77 × 10-191201
 
4.7%
0770
 
3.0%
2.79 × 10-19711
 
2.8%
2.7 × 10-19573
 
2.3%
2.75 × 10-19559
 
2.2%
1.11528
 
2.1%
1.02466
 
1.8%
2.76 × 10-19466
 
1.8%
1.01451
 
1.8%
Other values (395)18261
72.2%
ValueCountFrequency (%)
0770
3.0%
2.69 × 10-19205
 
0.8%
2.7 × 10-19573
2.3%
2.71 × 10-191300
5.1%
2.72 × 10-19369
 
1.5%
2.73 × 10-19284
 
1.1%
2.74 × 10-1928
 
0.1%
2.75 × 10-19559
2.2%
2.76 × 10-19466
 
1.8%
2.77 × 10-191201
4.7%
ValueCountFrequency (%)
1.11528
2.1%
1.1108
 
0.4%
1.09169
 
0.7%
1.08218
0.9%
1.07246
1.0%
1.06306
1.2%
1.05336
1.3%
1.04298
1.2%
1.03378
1.5%
1.02466
1.8%

S1_OutputCurrent
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean322.8068101
Minimum315
Maximum331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size197.7 KiB
2022-08-25T18:32:00.099610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum315
5-th percentile317
Q1320
median322
Q3327
95-th percentile330
Maximum331
Range16
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.211387274
Coefficient of variation (CV)0.01304615374
Kurtosis-0.7916209179
Mean322.8068101
Median Absolute Deviation (MAD)2
Skewness0.3356164566
Sum8162493
Variance17.73578277
MonotonicityNot monotonic
2022-08-25T18:32:00.254580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3233392
13.4%
3212435
9.6%
3242434
9.6%
3222335
9.2%
3202137
8.5%
3192013
8.0%
3271787
7.1%
3301760
7.0%
3181675
6.6%
3291559
6.2%
Other values (7)3759
14.9%
ValueCountFrequency (%)
315543
 
2.1%
316614
 
2.4%
3171140
 
4.5%
3181675
6.6%
3192013
8.0%
3202137
8.5%
3212435
9.6%
3222335
9.2%
3233392
13.4%
3242434
9.6%
ValueCountFrequency (%)
331764
 
3.0%
3301760
7.0%
3291559
6.2%
328611
 
2.4%
3271787
7.1%
32643
 
0.2%
32544
 
0.2%
3242434
9.6%
3233392
13.4%
3222335
9.2%

S1_OutputVoltage
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct117
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.46242692
Minimum0
Maximum122
Zeros6876
Zeros (%)27.2%
Negative0
Negative (%)0.0%
Memory size197.7 KiB
2022-08-25T18:32:00.399051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median117
Q3119
95-th percentile121
Maximum122
Range122
Interquartile range (IQR)119

Descriptive statistics

Standard deviation52.51969461
Coefficient of variation (CV)0.6145354924
Kurtosis-0.9777352383
Mean85.46242692
Median Absolute Deviation (MAD)2
Skewness-1.006548635
Sum2161002.927
Variance2758.318322
MonotonicityNot monotonic
2022-08-25T18:32:00.551032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06876
27.2%
1183386
13.4%
1173035
12.0%
1192873
11.4%
1162363
 
9.3%
1202113
 
8.4%
1151498
 
5.9%
1211222
 
4.8%
114717
 
2.8%
122714
 
2.8%
Other values (107)489
 
1.9%
ValueCountFrequency (%)
06876
27.2%
0.3811
 
< 0.1%
0.6241
 
< 0.1%
0.8681
 
< 0.1%
0.9741
 
< 0.1%
1.021
 
< 0.1%
1.193
 
< 0.1%
1.21
 
< 0.1%
1.271
 
< 0.1%
1.281
 
< 0.1%
ValueCountFrequency (%)
122714
 
2.8%
1211222
 
4.8%
1202113
8.4%
1192873
11.4%
1183386
13.4%
1173035
12.0%
1162363
9.3%
1151498
5.9%
114717
 
2.8%
113255
 
1.0%

S1_OutputPower
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1955
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1268646339
Minimum-1.080675 × 10-5
Maximum0.212
Zeros2045
Zeros (%)8.1%
Negative2301
Negative (%)9.1%
Memory size197.7 KiB
2022-08-25T18:32:00.703876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-1.080675 × 10-5
5-th percentile-2.32 × 10-6
Q15.3125 × 10-6
median0.164
Q30.183
95-th percentile0.205
Maximum0.212
Range0.2120108068
Interquartile range (IQR)0.1829946875

Descriptive statistics

Standard deviation0.07953620894
Coefficient of variation (CV)0.6269375988
Kurtosis-1.040140021
Mean0.1268646339
Median Absolute Deviation (MAD)0.023
Skewness-0.8747248731
Sum3207.899132
Variance0.006326008533
MonotonicityNot monotonic
2022-08-25T18:32:00.817585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02045
 
8.1%
0.212550
 
2.2%
0.173385
 
1.5%
0.174374
 
1.5%
0.175369
 
1.5%
0.177365
 
1.4%
0.183363
 
1.4%
0.179363
 
1.4%
0.172359
 
1.4%
0.184357
 
1.4%
Other values (1945)19756
78.1%
ValueCountFrequency (%)
-1.080675 × 10-5253
1.0%
-1.08 × 10-51
 
< 0.1%
-1.06 × 10-51
 
< 0.1%
-1.05 × 10-51
 
< 0.1%
-1.04 × 10-51
 
< 0.1%
-1.02 × 10-52
 
< 0.1%
-1.01 × 10-51
 
< 0.1%
-9.97 × 10-61
 
< 0.1%
-9.91 × 10-61
 
< 0.1%
-9.64 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.212550
2.2%
0.21165
 
0.3%
0.2189
 
0.4%
0.20985
 
0.3%
0.208111
 
0.4%
0.207115
 
0.5%
0.206132
 
0.5%
0.205128
 
0.5%
0.204156
 
0.6%
0.203155
 
0.6%

S1_SystemInertia
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
12.0
25286 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters101144
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12.0
2nd row12.0
3rd row12.0
4th row12.0
5th row12.0

Common Values

ValueCountFrequency (%)
12.025286
100.0%

Length

2022-08-25T18:32:00.962827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-25T18:32:01.068294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
12.025286
100.0%

Most occurring characters

ValueCountFrequency (%)
125286
25.0%
225286
25.0%
.25286
25.0%
025286
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number75858
75.0%
Other Punctuation25286
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
125286
33.3%
225286
33.3%
025286
33.3%
Other Punctuation
ValueCountFrequency (%)
.25286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common101144
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
125286
25.0%
225286
25.0%
.25286
25.0%
025286
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII101144
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
125286
25.0%
225286
25.0%
.25286
25.0%
025286
25.0%

M1_CURRENT_PROGRAM_NUMBER
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1.0
24581 
0.0
 
705

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters75858
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.024581
97.2%
0.0705
 
2.8%

Length

2022-08-25T18:32:01.141301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-25T18:32:01.236938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.024581
97.2%
0.0705
 
2.8%

Most occurring characters

ValueCountFrequency (%)
025991
34.3%
.25286
33.3%
124581
32.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number50572
66.7%
Other Punctuation25286
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
025991
51.4%
124581
48.6%
Other Punctuation
ValueCountFrequency (%)
.25286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common75858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
025991
34.3%
.25286
33.3%
124581
32.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII75858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
025991
34.3%
.25286
33.3%
124581
32.4%

M1_sequence_number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct128
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.32650479
Minimum0
Maximum128
Zeros5706
Zeros (%)22.6%
Negative0
Negative (%)0.0%
Memory size197.7 KiB
2022-08-25T18:32:01.335724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median39
Q385
95-th percentile122
Maximum128
Range128
Interquartile range (IQR)83

Descriptive statistics

Standard deviation43.79146765
Coefficient of variation (CV)0.925305341
Kurtosis-1.277552339
Mean47.32650479
Median Absolute Deviation (MAD)39
Skewness0.3933402336
Sum1196698
Variance1917.692639
MonotonicityNot monotonic
2022-08-25T18:32:01.482459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05706
22.6%
22535
 
10.0%
128629
 
2.5%
7419
 
1.7%
87330
 
1.3%
22329
 
1.3%
29324
 
1.3%
46322
 
1.3%
71320
 
1.3%
81298
 
1.2%
Other values (118)14074
55.7%
ValueCountFrequency (%)
05706
22.6%
15
 
< 0.1%
22535
10.0%
32
 
< 0.1%
43
 
< 0.1%
55
 
< 0.1%
64
 
< 0.1%
7419
 
1.7%
82
 
< 0.1%
926
 
0.1%
ValueCountFrequency (%)
128629
2.5%
12758
 
0.2%
126147
 
0.6%
125109
 
0.4%
124186
 
0.7%
12397
 
0.4%
122229
 
0.9%
121142
 
0.6%
120191
 
0.8%
119158
 
0.6%

M1_CURRENT_FEEDRATE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.54203907
Minimum3
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size197.7 KiB
2022-08-25T18:32:01.580726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q13
median6
Q320
95-th percentile50
Maximum50
Range47
Interquartile range (IQR)17

Descriptive statistics

Standard deviation19.62021937
Coefficient of variation (CV)1.186082277
Kurtosis-0.7865397062
Mean16.54203907
Median Absolute Deviation (MAD)3
Skewness1.043191638
Sum418282
Variance384.9530082
MonotonicityNot monotonic
2022-08-25T18:32:01.662585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
311788
46.6%
506253
24.7%
64889
19.3%
201426
 
5.6%
12512
 
2.0%
15418
 
1.7%
ValueCountFrequency (%)
311788
46.6%
64889
19.3%
12512
 
2.0%
15418
 
1.7%
201426
 
5.6%
506253
24.7%
ValueCountFrequency (%)
506253
24.7%
201426
 
5.6%
15418
 
1.7%
12512
 
2.0%
64889
19.3%
311788
46.6%

Machining_Process
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.916356877
Minimum0
Maximum10
Zeros2585
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size98.9 KiB
2022-08-25T18:32:01.787105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.577461941
Coefficient of variation (CV)0.6581274439
Kurtosis-1.184367437
Mean3.916356877
Median Absolute Deviation (MAD)2
Skewness0.1408672494
Sum99029
Variance6.643310057
MonotonicityNot monotonic
2022-08-25T18:32:01.896298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
24085
16.2%
83377
13.4%
43104
12.3%
62794
11.0%
12655
10.5%
02585
10.2%
32528
10.0%
52354
9.3%
71795
7.1%
108
 
< 0.1%
ValueCountFrequency (%)
02585
10.2%
12655
10.5%
24085
16.2%
32528
10.0%
43104
12.3%
52354
9.3%
62794
11.0%
71795
7.1%
83377
13.4%
91
 
< 0.1%
ValueCountFrequency (%)
108
 
< 0.1%
91
 
< 0.1%
83377
13.4%
71795
7.1%
62794
11.0%
52354
9.3%
43104
12.3%
32528
10.0%
24085
16.2%
12655
10.5%

target
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
13308 
0
11978 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25286
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
113308
52.6%
011978
47.4%

Length

2022-08-25T18:32:01.991876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-25T18:32:02.124850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
113308
52.6%
011978
47.4%

Most occurring characters

ValueCountFrequency (%)
113308
52.6%
011978
47.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number25286
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
113308
52.6%
011978
47.4%

Most occurring scripts

ValueCountFrequency (%)
Common25286
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
113308
52.6%
011978
47.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII25286
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
113308
52.6%
011978
47.4%

Interactions

2022-08-25T18:31:39.886623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:26:33.226342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:26:43.214931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:26:52.152656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:26:58.780850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:06.412887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:12.030890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:19.501403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:28.403226image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:37.837518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:46.386780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:55.066483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:09.977511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:17.325482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:22.483645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:28.394296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:37.409933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:50.397334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:00.833876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:12.609550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:19.819866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:28.631135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:38.832100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:51.924344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:03.213915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:13.915338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:22.225342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:29.754865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:34.858166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:39.969894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:45.210735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:50.617773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:55.791058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:01.123830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:06.116240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:12.711017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:18.078747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:23.046554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:28.219583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:34.012359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:40.019640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:26:33.553604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:26:43.377288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:26:52.275987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:26:59.065159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:06.539398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:12.150242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:19.822000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:28.575425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:38.015869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:46.643641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:55.335666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:10.149826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:17.446597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:22.603046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:28.515882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:37.598781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:50.621833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:01.221525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:12.750108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:19.933080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:28.822000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:39.076034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:52.293246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:03.557153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:14.083889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:22.353832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:29.876260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:34.978163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:40.082394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:45.357760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:50.798172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:55.905601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:01.244375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:06.247257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:12.833312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:18.211109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:23.167695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:28.358242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:34.294317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:40.145295image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:26:33.805211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:26:43.610457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:26:52.419215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:26:59.329288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:06.658672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:12.261671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:20.034036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:28.696064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:38.175785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:46.842182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:55.601947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:10.317920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:17.588366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:22.721912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:28.650731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:37.804153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:50.948470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:01.586587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:12.907774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:20.068413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:29.089648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:39.320050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:52.885386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:03.886780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:14.326332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:22.482692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:30.006242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:35.101028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:40.226052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:45.523323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:50.919977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:56.021150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:01.361523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:06.366861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:12.966342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:18.329444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:23.278017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:28.468475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:34.497546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:31:40.264624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:26:34.070807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:26:43.747428image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:26:52.534189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:26:59.515901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:06.846859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:12.387133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:20.189536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:29.013319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:38.369541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:47.023154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:27:55.922526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:10.571875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:17.700642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:22.836150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:28.772272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:38.241450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:28:51.237131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:01.922362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:13.256389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:20.182262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:29.382561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:39.582388image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:29:53.182200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:04.262574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:14.567585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:22.602987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:30.151433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:35.210372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-25T18:30:40.342029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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